Overview

Dataset statistics

Number of variables24
Number of observations5144
Missing cells573
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory964.6 KiB
Average record size in memory192.0 B

Variable types

Numeric14
Categorical10

Warnings

R_fighter has a high cardinality: 1334 distinct values High cardinality
B_fighter has a high cardinality: 1774 distinct values High cardinality
Referee has a high cardinality: 190 distinct values High cardinality
date has a high cardinality: 476 distinct values High cardinality
location has a high cardinality: 157 distinct values High cardinality
Unnamed: 0 is highly correlated with #High correlation
# is highly correlated with Unnamed: 0High correlation
B_total_rounds_fought is highly correlated with B_winsHigh correlation
B_wins is highly correlated with B_total_rounds_foughtHigh correlation
R_total_rounds_fought is highly correlated with R_winsHigh correlation
R_wins is highly correlated with R_total_rounds_foughtHigh correlation
B_Stance has 159 (3.1%) missing values Missing
R_Stance has 134 (2.6%) missing values Missing
B_age has 172 (3.3%) missing values Missing
R_age has 64 (1.2%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
# is uniformly distributed Uniform
date is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
# has unique values Unique
B_losses has 2036 (39.6%) zeros Zeros
B_total_rounds_fought has 1265 (24.6%) zeros Zeros
B_wins has 1813 (35.2%) zeros Zeros
R_losses has 1456 (28.3%) zeros Zeros
R_total_rounds_fought has 650 (12.6%) zeros Zeros
R_wins has 1065 (20.7%) zeros Zeros

Reproduction

Analysis started2021-03-02 15:07:30.763559
Analysis finished2021-03-02 15:08:38.130167
Duration1 minute and 7.37 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2571.5
Minimum0
Maximum5143
Zeros1
Zeros (%)< 0.1%
Memory size40.3 KiB
2021-03-02T16:08:38.363323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile257.15
Q11285.75
median2571.5
Q33857.25
95-th percentile4885.85
Maximum5143
Range5143
Interquartile range (IQR)2571.5

Descriptive statistics

Standard deviation1485.089223
Coefficient of variation (CV)0.5775186556
Kurtosis-1.2
Mean2571.5
Median Absolute Deviation (MAD)1286
Skewness0
Sum13227796
Variance2205490
MonotocityStrictly increasing
2021-03-02T16:08:38.674618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
6411
 
< 0.1%
26121
 
< 0.1%
5651
 
< 0.1%
46631
 
< 0.1%
26161
 
< 0.1%
5691
 
< 0.1%
46671
 
< 0.1%
26201
 
< 0.1%
5731
 
< 0.1%
Other values (5134)5134
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
51431
< 0.1%
51421
< 0.1%
51411
< 0.1%
51401
< 0.1%
51391
< 0.1%

#
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2571.5
Minimum0
Maximum5143
Zeros1
Zeros (%)< 0.1%
Memory size40.3 KiB
2021-03-02T16:08:38.971513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile257.15
Q11285.75
median2571.5
Q33857.25
95-th percentile4885.85
Maximum5143
Range5143
Interquartile range (IQR)2571.5

Descriptive statistics

Standard deviation1485.089223
Coefficient of variation (CV)0.5775186556
Kurtosis-1.2
Mean2571.5
Median Absolute Deviation (MAD)1286
Skewness0
Sum13227796
Variance2205490
MonotocityStrictly increasing
2021-03-02T16:08:39.319343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
6411
 
< 0.1%
26121
 
< 0.1%
5651
 
< 0.1%
46631
 
< 0.1%
26161
 
< 0.1%
5691
 
< 0.1%
46671
 
< 0.1%
26201
 
< 0.1%
5731
 
< 0.1%
Other values (5134)5134
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
51431
< 0.1%
51421
< 0.1%
51411
< 0.1%
51401
< 0.1%
51391
< 0.1%

R_fighter
Categorical

HIGH CARDINALITY

Distinct1334
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
Jim Miller
 
23
Donald Cerrone
 
22
Diego Sanchez
 
21
Demian Maia
 
21
Michael Bisping
 
21
Other values (1329)
5036 

Length

Max length25
Median length13
Mean length12.88374806
Min length7

Characters and Unicode

Total characters66274
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)8.6%

Sample

1st rowHenry Cejudo
2nd rowValentina Shevchenko
3rd rowTony Ferguson
4th rowJimmie Rivera
5th rowTai Tuivasa
ValueCountFrequency (%)
Jim Miller23
 
0.4%
Donald Cerrone22
 
0.4%
Diego Sanchez21
 
0.4%
Demian Maia21
 
0.4%
Michael Bisping21
 
0.4%
Matt Hughes20
 
0.4%
Anderson Silva20
 
0.4%
Frank Mir19
 
0.4%
Joe Lauzon19
 
0.4%
Ross Pearson19
 
0.4%
Other values (1324)4939
96.0%
2021-03-02T16:08:40.045549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
matt87
 
0.8%
chris71
 
0.7%
john67
 
0.6%
silva66
 
0.6%
mike63
 
0.6%
thiago62
 
0.6%
mark56
 
0.5%
johnson54
 
0.5%
joe53
 
0.5%
michael50
 
0.5%
Other values (1890)9834
94.0%

Most occurring characters

ValueCountFrequency (%)
a6349
 
9.6%
e5527
 
8.3%
5319
 
8.0%
n4585
 
6.9%
i4463
 
6.7%
o4367
 
6.6%
r4326
 
6.5%
l2686
 
4.1%
s2679
 
4.0%
t2095
 
3.2%
Other values (46)23878
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50266
75.8%
Uppercase Letter10641
 
16.1%
Space Separator5319
 
8.0%
Dash Punctuation36
 
0.1%
Other Punctuation12
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
M1124
 
10.6%
J924
 
8.7%
S872
 
8.2%
C755
 
7.1%
D692
 
6.5%
R675
 
6.3%
B621
 
5.8%
A620
 
5.8%
T540
 
5.1%
P488
 
4.6%
Other values (16)3330
31.3%
ValueCountFrequency (%)
a6349
12.6%
e5527
11.0%
n4585
 
9.1%
i4463
 
8.9%
o4367
 
8.7%
r4326
 
8.6%
l2686
 
5.3%
s2679
 
5.3%
t2095
 
4.2%
u1636
 
3.3%
Other values (16)11553
23.0%
ValueCountFrequency (%)
'8
66.7%
.4
33.3%
ValueCountFrequency (%)
5319
100.0%
ValueCountFrequency (%)
-36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60907
91.9%
Common5367
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
a6349
 
10.4%
e5527
 
9.1%
n4585
 
7.5%
i4463
 
7.3%
o4367
 
7.2%
r4326
 
7.1%
l2686
 
4.4%
s2679
 
4.4%
t2095
 
3.4%
u1636
 
2.7%
Other values (42)22194
36.4%
ValueCountFrequency (%)
5319
99.1%
-36
 
0.7%
'8
 
0.1%
.4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII66274
100.0%

Most frequent character per block

ValueCountFrequency (%)
a6349
 
9.6%
e5527
 
8.3%
5319
 
8.0%
n4585
 
6.9%
i4463
 
6.7%
o4367
 
6.6%
r4326
 
6.5%
l2686
 
4.1%
s2679
 
4.0%
t2095
 
3.2%
Other values (46)23878
36.0%

B_fighter
Categorical

HIGH CARDINALITY

Distinct1774
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
Jeremy Stephens
 
19
Charles Oliveira
 
17
Nik Lentz
 
14
Rafael Dos Anjos
 
13
Tim Boetsch
 
13
Other values (1769)
5068 

Length

Max length25
Median length13
Mean length12.90221617
Min length7

Characters and Unicode

Total characters66369
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique553 ?
Unique (%)10.8%

Sample

1st rowMarlon Moraes
2nd rowJessica Eye
3rd rowDonald Cerrone
4th rowPetr Yan
5th rowBlagoy Ivanov
ValueCountFrequency (%)
Jeremy Stephens19
 
0.4%
Charles Oliveira17
 
0.3%
Nik Lentz14
 
0.3%
Rafael Dos Anjos13
 
0.3%
Tim Boetsch13
 
0.3%
Kevin Lee12
 
0.2%
Rick Story12
 
0.2%
Chris Lytle12
 
0.2%
Evan Dunham12
 
0.2%
Gleison Tibau12
 
0.2%
Other values (1764)5008
97.4%
2021-03-02T16:08:40.717592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chris85
 
0.8%
mike73
 
0.7%
matt70
 
0.7%
john61
 
0.6%
jason57
 
0.5%
joe54
 
0.5%
alex54
 
0.5%
anthony53
 
0.5%
tim51
 
0.5%
silva49
 
0.5%
Other values (2354)9856
94.2%

Most occurring characters

ValueCountFrequency (%)
a6330
 
9.5%
e5627
 
8.5%
5319
 
8.0%
n4626
 
7.0%
i4469
 
6.7%
o4405
 
6.6%
r4270
 
6.4%
l2873
 
4.3%
s2648
 
4.0%
t2175
 
3.3%
Other values (46)23627
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50411
76.0%
Uppercase Letter10591
 
16.0%
Space Separator5319
 
8.0%
Dash Punctuation29
 
< 0.1%
Other Punctuation19
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
M1020
 
9.6%
J911
 
8.6%
S896
 
8.5%
C801
 
7.6%
A701
 
6.6%
D668
 
6.3%
R657
 
6.2%
B607
 
5.7%
T488
 
4.6%
P463
 
4.4%
Other values (16)3379
31.9%
ValueCountFrequency (%)
a6330
12.6%
e5627
11.2%
n4626
9.2%
i4469
 
8.9%
o4405
 
8.7%
r4270
 
8.5%
l2873
 
5.7%
s2648
 
5.3%
t2175
 
4.3%
h1639
 
3.3%
Other values (16)11349
22.5%
ValueCountFrequency (%)
'15
78.9%
.4
 
21.1%
ValueCountFrequency (%)
5319
100.0%
ValueCountFrequency (%)
-29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61002
91.9%
Common5367
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
a6330
 
10.4%
e5627
 
9.2%
n4626
 
7.6%
i4469
 
7.3%
o4405
 
7.2%
r4270
 
7.0%
l2873
 
4.7%
s2648
 
4.3%
t2175
 
3.6%
h1639
 
2.7%
Other values (42)21940
36.0%
ValueCountFrequency (%)
5319
99.1%
-29
 
0.5%
'15
 
0.3%
.4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII66369
100.0%

Most frequent character per block

ValueCountFrequency (%)
a6330
 
9.5%
e5627
 
8.5%
5319
 
8.0%
n4626
 
7.0%
i4469
 
6.7%
o4405
 
6.6%
r4270
 
6.4%
l2873
 
4.3%
s2648
 
4.0%
t2175
 
3.3%
Other values (46)23627
35.6%

Referee
Categorical

HIGH CARDINALITY

Distinct190
Distinct (%)3.7%
Missing23
Missing (%)0.4%
Memory size40.3 KiB
Herb Dean
726 
John McCarthy
634 
Mario Yamasaki
391 
Dan Miragliotta
347 
Marc Goddard
276 
Other values (185)
2747 

Length

Max length20
Median length13
Mean length12.51318102
Min length8

Characters and Unicode

Total characters64080
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.3%

Sample

1st rowMarc Goddard
2nd rowRobert Madrigal
3rd rowDan Miragliotta
4th rowKevin MacDonald
5th rowDan Miragliotta
ValueCountFrequency (%)
Herb Dean726
 
14.1%
John McCarthy634
 
12.3%
Mario Yamasaki391
 
7.6%
Dan Miragliotta347
 
6.7%
Marc Goddard276
 
5.4%
Yves Lavigne259
 
5.0%
Steve Mazzagatti201
 
3.9%
Leon Roberts179
 
3.5%
Keith Peterson139
 
2.7%
Josh Rosenthal119
 
2.3%
Other values (180)1850
36.0%
2021-03-02T16:08:41.529960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dean726
 
7.1%
herb726
 
7.1%
john702
 
6.8%
mccarthy634
 
6.2%
yamasaki406
 
3.9%
mario391
 
3.8%
dan353
 
3.4%
miragliotta347
 
3.4%
steve284
 
2.8%
marc283
 
2.8%
Other values (306)5433
52.8%

Most occurring characters

ValueCountFrequency (%)
a7511
 
11.7%
e5290
 
8.3%
5164
 
8.1%
r4626
 
7.2%
n4053
 
6.3%
o3891
 
6.1%
i3543
 
5.5%
t3173
 
5.0%
M2341
 
3.7%
h2223
 
3.5%
Other values (41)22265
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47896
74.7%
Uppercase Letter11012
 
17.2%
Space Separator5164
 
8.1%
Dash Punctuation8
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a7511
15.7%
e5290
11.0%
r4626
9.7%
n4053
8.5%
o3891
8.1%
i3543
 
7.4%
t3173
 
6.6%
h2223
 
4.6%
s1997
 
4.2%
l1925
 
4.0%
Other values (16)9664
20.2%
ValueCountFrequency (%)
M2341
21.3%
D1212
11.0%
J1158
10.5%
H1002
9.1%
C880
 
8.0%
Y665
 
6.0%
L547
 
5.0%
S508
 
4.6%
G465
 
4.2%
K464
 
4.2%
Other values (13)1770
16.1%
ValueCountFrequency (%)
5164
100.0%
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58908
91.9%
Common5172
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
a7511
 
12.8%
e5290
 
9.0%
r4626
 
7.9%
n4053
 
6.9%
o3891
 
6.6%
i3543
 
6.0%
t3173
 
5.4%
M2341
 
4.0%
h2223
 
3.8%
s1997
 
3.4%
Other values (39)20260
34.4%
ValueCountFrequency (%)
5164
99.8%
-8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64080
100.0%

Most frequent character per block

ValueCountFrequency (%)
a7511
 
11.7%
e5290
 
8.3%
5164
 
8.1%
r4626
 
7.2%
n4053
 
6.3%
o3891
 
6.1%
i3543
 
5.5%
t3173
 
5.0%
M2341
 
3.7%
h2223
 
3.5%
Other values (41)22265
34.7%

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct476
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
2016-11-19
 
25
2014-10-04
 
23
2014-05-31
 
22
2014-08-23
 
21
2014-06-28
 
21
Other values (471)
5032 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters51440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-06-08
2nd row2019-06-08
3rd row2019-06-08
4th row2019-06-08
5th row2019-06-08
ValueCountFrequency (%)
2016-11-1925
 
0.5%
2014-10-0423
 
0.4%
2014-05-3122
 
0.4%
2014-08-2321
 
0.4%
2014-06-2821
 
0.4%
1994-03-1115
 
0.3%
2018-04-1414
 
0.3%
2018-09-2214
 
0.3%
2014-04-1613
 
0.3%
2016-04-1013
 
0.3%
Other values (466)4963
96.5%
2021-03-02T16:08:42.207420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-11-1925
 
0.5%
2014-10-0423
 
0.4%
2014-05-3122
 
0.4%
2014-08-2321
 
0.4%
2014-06-2821
 
0.4%
1994-03-1115
 
0.3%
2018-04-1414
 
0.3%
2018-09-2214
 
0.3%
2014-04-1613
 
0.3%
2016-04-1013
 
0.3%
Other values (466)4963
96.5%

Most occurring characters

ValueCountFrequency (%)
012442
24.2%
-10288
20.0%
19021
17.5%
28278
16.1%
91823
 
3.5%
61712
 
3.3%
71678
 
3.3%
81615
 
3.1%
41545
 
3.0%
31542
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41152
80.0%
Dash Punctuation10288
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
012442
30.2%
19021
21.9%
28278
20.1%
91823
 
4.4%
61712
 
4.2%
71678
 
4.1%
81615
 
3.9%
41545
 
3.8%
31542
 
3.7%
51496
 
3.6%
ValueCountFrequency (%)
-10288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common51440
100.0%

Most frequent character per script

ValueCountFrequency (%)
012442
24.2%
-10288
20.0%
19021
17.5%
28278
16.1%
91823
 
3.5%
61712
 
3.3%
71678
 
3.3%
81615
 
3.1%
41545
 
3.0%
31542
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII51440
100.0%

Most frequent character per block

ValueCountFrequency (%)
012442
24.2%
-10288
20.0%
19021
17.5%
28278
16.1%
91823
 
3.5%
61712
 
3.3%
71678
 
3.3%
81615
 
3.1%
41545
 
3.0%
31542
 
3.0%

location
Categorical

HIGH CARDINALITY

Distinct157
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
Las Vegas, Nevada, USA
1216 
London, England, United Kingdom
 
114
Chicago, Illinois, USA
 
81
Montreal, Quebec, Canada
 
81
Atlantic City, New Jersey, USA
 
80
Other values (152)
3572 

Length

Max length43
Median length23
Mean length24.58300933
Min length12

Characters and Unicode

Total characters126455
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChicago, Illinois, USA
2nd rowChicago, Illinois, USA
3rd rowChicago, Illinois, USA
4th rowChicago, Illinois, USA
5th rowChicago, Illinois, USA
ValueCountFrequency (%)
Las Vegas, Nevada, USA1216
 
23.6%
London, England, United Kingdom114
 
2.2%
Chicago, Illinois, USA81
 
1.6%
Montreal, Quebec, Canada81
 
1.6%
Atlantic City, New Jersey, USA80
 
1.6%
Los Angeles, California, USA79
 
1.5%
Newark, New Jersey, USA78
 
1.5%
Toronto, Ontario, Canada74
 
1.4%
Denver, Colorado, USA74
 
1.4%
Anaheim, California, USA72
 
1.4%
Other values (147)3195
62.1%
2021-03-02T16:08:42.999619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa3392
 
18.2%
vegas1216
 
6.5%
las1216
 
6.5%
nevada1216
 
6.5%
new496
 
2.7%
brazil405
 
2.2%
canada342
 
1.8%
california303
 
1.6%
united273
 
1.5%
kingdom255
 
1.4%
Other values (263)9551
51.2%

Most occurring characters

ValueCountFrequency (%)
a14351
 
11.3%
13521
 
10.7%
,9838
 
7.8%
e8456
 
6.7%
i6418
 
5.1%
n6102
 
4.8%
o5952
 
4.7%
s5598
 
4.4%
r4456
 
3.5%
l4447
 
3.5%
Other values (45)47316
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter77730
61.5%
Uppercase Letter25294
 
20.0%
Space Separator13521
 
10.7%
Other Punctuation9863
 
7.8%
Dash Punctuation47
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a14351
18.5%
e8456
10.9%
i6418
8.3%
n6102
7.9%
o5952
7.7%
s5598
 
7.2%
r4456
 
5.7%
l4447
 
5.7%
d3797
 
4.9%
t3516
 
4.5%
Other values (16)14637
18.8%
ValueCountFrequency (%)
S4403
17.4%
A4219
16.7%
U3744
14.8%
N2088
8.3%
L1616
 
6.4%
C1569
 
6.2%
V1343
 
5.3%
B906
 
3.6%
M674
 
2.7%
J505
 
2.0%
Other values (15)4227
16.7%
ValueCountFrequency (%)
,9838
99.7%
.25
 
0.3%
ValueCountFrequency (%)
13521
100.0%
ValueCountFrequency (%)
-47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin103024
81.5%
Common23431
 
18.5%

Most frequent character per script

ValueCountFrequency (%)
a14351
 
13.9%
e8456
 
8.2%
i6418
 
6.2%
n6102
 
5.9%
o5952
 
5.8%
s5598
 
5.4%
r4456
 
4.3%
l4447
 
4.3%
S4403
 
4.3%
A4219
 
4.1%
Other values (41)38622
37.5%
ValueCountFrequency (%)
13521
57.7%
,9838
42.0%
-47
 
0.2%
.25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII126455
100.0%

Most frequent character per block

ValueCountFrequency (%)
a14351
 
11.3%
13521
 
10.7%
,9838
 
7.8%
e8456
 
6.7%
i6418
 
5.1%
n6102
 
4.8%
o5952
 
4.7%
s5598
 
4.4%
r4456
 
3.5%
l4447
 
3.5%
Other values (45)47316
37.4%

Winner
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
Red
3470 
Blue
1591 
Draw
 
83

Length

Max length4
Median length3
Mean length3.325427683
Min length3

Characters and Unicode

Total characters17106
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRed
2nd rowRed
3rd rowRed
4th rowBlue
5th rowBlue
ValueCountFrequency (%)
Red3470
67.5%
Blue1591
30.9%
Draw83
 
1.6%
2021-03-02T16:08:43.926854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T16:08:44.490994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
red3470
67.5%
blue1591
30.9%
draw83
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e5061
29.6%
R3470
20.3%
d3470
20.3%
B1591
 
9.3%
l1591
 
9.3%
u1591
 
9.3%
D83
 
0.5%
r83
 
0.5%
a83
 
0.5%
w83
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11962
69.9%
Uppercase Letter5144
30.1%

Most frequent character per category

ValueCountFrequency (%)
e5061
42.3%
d3470
29.0%
l1591
 
13.3%
u1591
 
13.3%
r83
 
0.7%
a83
 
0.7%
w83
 
0.7%
ValueCountFrequency (%)
R3470
67.5%
B1591
30.9%
D83
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin17106
100.0%

Most frequent character per script

ValueCountFrequency (%)
e5061
29.6%
R3470
20.3%
d3470
20.3%
B1591
 
9.3%
l1591
 
9.3%
u1591
 
9.3%
D83
 
0.5%
r83
 
0.5%
a83
 
0.5%
w83
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII17106
100.0%

Most frequent character per block

ValueCountFrequency (%)
e5061
29.6%
R3470
20.3%
d3470
20.3%
B1591
 
9.3%
l1591
 
9.3%
u1591
 
9.3%
D83
 
0.5%
r83
 
0.5%
a83
 
0.5%
w83
 
0.5%

weight_class
Categorical

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
Lightweight
989 
Welterweight
969 
Middleweight
725 
Heavyweight
507 
Light Heavyweight
502 
Other values (9)
1452 

Length

Max length21
Median length12
Mean length12.58942457
Min length9

Characters and Unicode

Total characters64760
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBantamweight
2nd rowWomen's Flyweight
3rd rowLightweight
4th rowBantamweight
5th rowHeavyweight
ValueCountFrequency (%)
Lightweight989
19.2%
Welterweight969
18.8%
Middleweight725
14.1%
Heavyweight507
9.9%
Light Heavyweight502
9.8%
Featherweight442
8.6%
Bantamweight379
 
7.4%
Flyweight187
 
3.6%
Women's Strawweight143
 
2.8%
Women's Bantamweight111
 
2.2%
Other values (4)190
 
3.7%
2021-03-02T16:08:45.596979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
heavyweight1009
16.6%
lightweight989
16.2%
welterweight969
15.9%
middleweight725
11.9%
light502
8.2%
bantamweight490
8.0%
featherweight452
7.4%
women's314
 
5.2%
flyweight237
 
3.9%
strawweight143
 
2.3%
Other values (3)260
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e10126
15.6%
t8727
13.5%
i7360
11.4%
h7125
11.0%
g6635
10.2%
w5157
8.0%
a2622
 
4.0%
l1931
 
3.0%
r1564
 
2.4%
L1491
 
2.3%
Other values (19)12022
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57410
88.7%
Uppercase Letter6090
 
9.4%
Space Separator946
 
1.5%
Other Punctuation314
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
e10126
17.6%
t8727
15.2%
i7360
12.8%
h7125
12.4%
g6635
11.6%
w5157
9.0%
a2622
 
4.6%
l1931
 
3.4%
r1564
 
2.7%
d1450
 
2.5%
Other values (8)4713
8.2%
ValueCountFrequency (%)
L1491
24.5%
W1413
23.2%
H1009
16.6%
M725
11.9%
F689
11.3%
B490
 
8.0%
S143
 
2.3%
O92
 
1.5%
C38
 
0.6%
ValueCountFrequency (%)
'314
100.0%
ValueCountFrequency (%)
946
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63500
98.1%
Common1260
 
1.9%

Most frequent character per script

ValueCountFrequency (%)
e10126
15.9%
t8727
13.7%
i7360
11.6%
h7125
11.2%
g6635
10.4%
w5157
8.1%
a2622
 
4.1%
l1931
 
3.0%
r1564
 
2.5%
L1491
 
2.3%
Other values (17)10762
16.9%
ValueCountFrequency (%)
946
75.1%
'314
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII64760
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10126
15.6%
t8727
13.5%
i7360
11.4%
h7125
11.0%
g6635
10.2%
w5157
8.0%
a2622
 
4.0%
l1931
 
3.0%
r1564
 
2.4%
L1491
 
2.3%
Other values (19)12022
18.6%

no_of_rounds
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
3
4523 
5
 
423
2
 
98
1
 
78
4
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5144
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
34523
87.9%
5423
 
8.2%
298
 
1.9%
178
 
1.5%
422
 
0.4%
2021-03-02T16:08:46.414864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T16:08:46.757853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
34523
87.9%
5423
 
8.2%
298
 
1.9%
178
 
1.5%
422
 
0.4%

Most occurring characters

ValueCountFrequency (%)
34523
87.9%
5423
 
8.2%
298
 
1.9%
178
 
1.5%
422
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5144
100.0%

Most frequent character per category

ValueCountFrequency (%)
34523
87.9%
5423
 
8.2%
298
 
1.9%
178
 
1.5%
422
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common5144
100.0%

Most frequent character per script

ValueCountFrequency (%)
34523
87.9%
5423
 
8.2%
298
 
1.9%
178
 
1.5%
422
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5144
100.0%

Most frequent character per block

ValueCountFrequency (%)
34523
87.9%
5423
 
8.2%
298
 
1.9%
178
 
1.5%
422
 
0.4%

B_losses
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.463646967
Minimum0
Maximum13
Zeros2036
Zeros (%)39.6%
Memory size40.3 KiB
2021-03-02T16:08:47.372507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.866390831
Coefficient of variation (CV)1.275164622
Kurtosis4.659612314
Mean1.463646967
Median Absolute Deviation (MAD)1
Skewness1.930102916
Sum7529
Variance3.483414735
MonotocityNot monotonic
2021-03-02T16:08:47.877713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
02036
39.6%
11293
25.1%
2754
 
14.7%
3426
 
8.3%
4276
 
5.4%
5133
 
2.6%
683
 
1.6%
755
 
1.1%
837
 
0.7%
926
 
0.5%
Other values (4)25
 
0.5%
ValueCountFrequency (%)
02036
39.6%
11293
25.1%
2754
 
14.7%
3426
 
8.3%
4276
 
5.4%
ValueCountFrequency (%)
132
 
< 0.1%
122
 
< 0.1%
118
 
0.2%
1013
0.3%
926
0.5%

B_total_rounds_fought
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.920878694
Minimum0
Maximum75
Zeros1265
Zeros (%)24.6%
Memory size40.3 KiB
2021-03-02T16:08:48.479350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q313
95-th percentile33
Maximum75
Range75
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.26933966
Coefficient of variation (CV)1.263254444
Kurtosis5.142982796
Mean8.920878694
Median Absolute Deviation (MAD)5
Skewness2.064128217
Sum45889
Variance126.9980163
MonotocityNot monotonic
2021-03-02T16:08:48.920393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01265
24.6%
3486
 
9.4%
1296
 
5.8%
6273
 
5.3%
4228
 
4.4%
2208
 
4.0%
7188
 
3.7%
5185
 
3.6%
9168
 
3.3%
8150
 
2.9%
Other values (60)1697
33.0%
ValueCountFrequency (%)
01265
24.6%
1296
 
5.8%
2208
 
4.0%
3486
 
9.4%
4228
 
4.4%
ValueCountFrequency (%)
751
 
< 0.1%
731
 
< 0.1%
693
0.1%
684
0.1%
671
 
< 0.1%

B_wins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4844479
Minimum0
Maximum23
Zeros1813
Zeros (%)35.2%
Memory size40.3 KiB
2021-03-02T16:08:49.449406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.216860714
Coefficient of variation (CV)1.294799023
Kurtosis4.017032418
Mean2.4844479
Median Absolute Deviation (MAD)1
Skewness1.880474344
Sum12780
Variance10.34819285
MonotocityNot monotonic
2021-03-02T16:08:49.730822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
01813
35.2%
1933
18.1%
2612
 
11.9%
3462
 
9.0%
4327
 
6.4%
5214
 
4.2%
6204
 
4.0%
7140
 
2.7%
8114
 
2.2%
9101
 
2.0%
Other values (13)224
 
4.4%
ValueCountFrequency (%)
01813
35.2%
1933
18.1%
2612
 
11.9%
3462
 
9.0%
4327
 
6.4%
ValueCountFrequency (%)
231
 
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
193
0.1%
181
 
< 0.1%

B_Stance
Categorical

MISSING

Distinct5
Distinct (%)0.1%
Missing159
Missing (%)3.1%
Memory size40.3 KiB
Orthodox
3829 
Southpaw
975 
Switch
 
168
Open Stance
 
9
Sideways
 
4

Length

Max length11
Median length8
Mean length7.938014042
Min length6

Characters and Unicode

Total characters39571
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthodox
2nd rowOrthodox
3rd rowOrthodox
4th rowSwitch
5th rowSouthpaw
ValueCountFrequency (%)
Orthodox3829
74.4%
Southpaw975
 
19.0%
Switch168
 
3.3%
Open Stance9
 
0.2%
Sideways4
 
0.1%
(Missing)159
 
3.1%
2021-03-02T16:08:51.094818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T16:08:51.313730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox3829
76.7%
southpaw975
 
19.5%
switch168
 
3.4%
open9
 
0.2%
stance9
 
0.2%
sideways4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o8633
21.8%
t4981
12.6%
h4972
12.6%
O3838
9.7%
d3833
9.7%
r3829
9.7%
x3829
9.7%
S1156
 
2.9%
w1147
 
2.9%
a988
 
2.5%
Other values (9)2365
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34568
87.4%
Uppercase Letter4994
 
12.6%
Space Separator9
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o8633
25.0%
t4981
14.4%
h4972
14.4%
d3833
11.1%
r3829
11.1%
x3829
11.1%
w1147
 
3.3%
a988
 
2.9%
p984
 
2.8%
u975
 
2.8%
Other values (6)397
 
1.1%
ValueCountFrequency (%)
O3838
76.9%
S1156
 
23.1%
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39562
> 99.9%
Common9
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o8633
21.8%
t4981
12.6%
h4972
12.6%
O3838
9.7%
d3833
9.7%
r3829
9.7%
x3829
9.7%
S1156
 
2.9%
w1147
 
2.9%
a988
 
2.5%
Other values (8)2356
 
6.0%
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39571
100.0%

Most frequent character per block

ValueCountFrequency (%)
o8633
21.8%
t4981
12.6%
h4972
12.6%
O3838
9.7%
d3833
9.7%
r3829
9.7%
x3829
9.7%
S1156
 
2.9%
w1147
 
2.9%
a988
 
2.5%
Other values (9)2365
 
6.0%

B_Height_cms
Real number (ℝ≥0)

Distinct23
Distinct (%)0.4%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean179.238641
Minimum152.4
Maximum210.82
Zeros0
Zeros (%)0.0%
Memory size40.3 KiB
2021-03-02T16:08:51.640142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum152.4
5-th percentile165.1
Q1172.72
median180.34
Q3185.42
95-th percentile193.04
Maximum210.82
Range58.42
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation8.515038584
Coefficient of variation (CV)0.04750671249
Kurtosis0.03790201698
Mean179.238641
Median Absolute Deviation (MAD)5.08
Skewness-0.1024722873
Sum920569.66
Variance72.50588209
MonotocityNot monotonic
2021-03-02T16:08:51.874534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
182.88638
12.4%
177.8608
11.8%
180.34518
10.1%
185.42498
9.7%
175.26493
9.6%
172.72425
8.3%
187.96384
7.5%
190.5345
6.7%
170.18309
6.0%
167.64302
5.9%
Other values (13)616
12.0%
ValueCountFrequency (%)
152.43
 
0.1%
154.9425
 
0.5%
157.489
 
0.2%
160.0274
1.4%
162.5680
1.6%
ValueCountFrequency (%)
210.827
 
0.1%
208.286
 
0.1%
203.213
0.3%
200.6613
0.3%
198.1223
0.4%

B_Weight_lbs
Real number (ℝ≥0)

Distinct73
Distinct (%)1.4%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean172.1103542
Minimum115
Maximum770
Zeros0
Zeros (%)0.0%
Memory size40.3 KiB
2021-03-02T16:08:52.155664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile125
Q1145
median170
Q3185
95-th percentile250
Maximum770
Range655
Interquartile range (IQR)40

Descriptive statistics

Standard deviation36.8470219
Coefficient of variation (CV)0.2140895129
Kurtosis14.9600525
Mean172.1103542
Median Absolute Deviation (MAD)15
Skewness1.851543608
Sum884303
Variance1357.703023
MonotocityNot monotonic
2021-03-02T16:08:52.468184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1701018
19.8%
155941
18.3%
185682
13.3%
205519
10.1%
135507
9.9%
145462
9.0%
125266
 
5.2%
115137
 
2.7%
265100
 
1.9%
24049
 
1.0%
Other values (63)457
8.9%
ValueCountFrequency (%)
115137
 
2.7%
125266
 
5.2%
135507
9.9%
145462
9.0%
155941
18.3%
ValueCountFrequency (%)
7701
< 0.1%
4301
< 0.1%
4101
< 0.1%
4001
< 0.1%
3501
< 0.1%

R_losses
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.951399689
Minimum0
Maximum14
Zeros1456
Zeros (%)28.3%
Memory size40.3 KiB
2021-03-02T16:08:52.733955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.11821769
Coefficient of variation (CV)1.085486332
Kurtosis3.25954856
Mean1.951399689
Median Absolute Deviation (MAD)1
Skewness1.624273114
Sum10038
Variance4.486846184
MonotocityNot monotonic
2021-03-02T16:08:52.952592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
01456
28.3%
11284
25.0%
2888
17.3%
3584
11.4%
4335
 
6.5%
5233
 
4.5%
6142
 
2.8%
775
 
1.5%
866
 
1.3%
941
 
0.8%
Other values (5)40
 
0.8%
ValueCountFrequency (%)
01456
28.3%
11284
25.0%
2888
17.3%
3584
11.4%
4335
 
6.5%
ValueCountFrequency (%)
142
 
< 0.1%
135
 
0.1%
127
0.1%
1110
0.2%
1016
0.3%

R_total_rounds_fought
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.85342146
Minimum0
Maximum80
Zeros650
Zeros (%)12.6%
Memory size40.3 KiB
2021-03-02T16:08:53.345753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q319
95-th percentile41
Maximum80
Range80
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.36935096
Coefficient of variation (CV)1.040139468
Kurtosis2.691781092
Mean12.85342146
Median Absolute Deviation (MAD)7
Skewness1.569306585
Sum66118
Variance178.739545
MonotocityNot monotonic
2021-03-02T16:08:53.727724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0650
 
12.6%
3412
 
8.0%
6267
 
5.2%
1253
 
4.9%
4227
 
4.4%
5204
 
4.0%
2194
 
3.8%
7191
 
3.7%
9190
 
3.7%
10159
 
3.1%
Other values (69)2397
46.6%
ValueCountFrequency (%)
0650
12.6%
1253
 
4.9%
2194
 
3.8%
3412
8.0%
4227
 
4.4%
ValueCountFrequency (%)
801
< 0.1%
791
< 0.1%
782
< 0.1%
771
< 0.1%
761
< 0.1%

R_wins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.598172628
Minimum0
Maximum20
Zeros1065
Zeros (%)20.7%
Memory size40.3 KiB
2021-03-02T16:08:54.096404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile11
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.709519283
Coefficient of variation (CV)1.030945334
Kurtosis1.56652001
Mean3.598172628
Median Absolute Deviation (MAD)2
Skewness1.344304168
Sum18509
Variance13.76053331
MonotocityNot monotonic
2021-03-02T16:08:54.346421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01065
20.7%
1884
17.2%
2648
12.6%
3545
10.6%
4421
 
8.2%
5335
 
6.5%
6259
 
5.0%
7213
 
4.1%
8171
 
3.3%
9159
 
3.1%
Other values (11)444
8.6%
ValueCountFrequency (%)
01065
20.7%
1884
17.2%
2648
12.6%
3545
10.6%
4421
 
8.2%
ValueCountFrequency (%)
204
 
0.1%
197
 
0.1%
187
 
0.1%
1710
0.2%
1621
0.4%

R_Stance
Categorical

MISSING

Distinct5
Distinct (%)0.1%
Missing134
Missing (%)2.6%
Memory size40.3 KiB
Orthodox
3807 
Southpaw
1036 
Switch
 
150
Open Stance
 
15
Sideways
 
2

Length

Max length11
Median length8
Mean length7.949101796
Min length6

Characters and Unicode

Total characters39825
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthodox
2nd rowSouthpaw
3rd rowOrthodox
4th rowOrthodox
5th rowSouthpaw
ValueCountFrequency (%)
Orthodox3807
74.0%
Southpaw1036
 
20.1%
Switch150
 
2.9%
Open Stance15
 
0.3%
Sideways2
 
< 0.1%
(Missing)134
 
2.6%
2021-03-02T16:08:54.940205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T16:08:55.158972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox3807
75.8%
southpaw1036
 
20.6%
switch150
 
3.0%
open15
 
0.3%
stance15
 
0.3%
sideways2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o8650
21.7%
t5008
12.6%
h4993
12.5%
O3822
9.6%
d3809
9.6%
r3807
9.6%
x3807
9.6%
S1203
 
3.0%
w1188
 
3.0%
a1053
 
2.6%
Other values (9)2485
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34785
87.3%
Uppercase Letter5025
 
12.6%
Space Separator15
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o8650
24.9%
t5008
14.4%
h4993
14.4%
d3809
11.0%
r3807
10.9%
x3807
10.9%
w1188
 
3.4%
a1053
 
3.0%
p1051
 
3.0%
u1036
 
3.0%
Other values (6)383
 
1.1%
ValueCountFrequency (%)
O3822
76.1%
S1203
 
23.9%
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39810
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o8650
21.7%
t5008
12.6%
h4993
12.5%
O3822
9.6%
d3809
9.6%
r3807
9.6%
x3807
9.6%
S1203
 
3.0%
w1188
 
3.0%
a1053
 
2.6%
Other values (8)2470
 
6.2%
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39825
100.0%

Most frequent character per block

ValueCountFrequency (%)
o8650
21.7%
t5008
12.6%
h4993
12.5%
O3822
9.6%
d3809
9.6%
r3807
9.6%
x3807
9.6%
S1203
 
3.0%
w1188
 
3.0%
a1053
 
2.6%
Other values (9)2485
 
6.2%

R_Height_cms
Real number (ℝ≥0)

Distinct23
Distinct (%)0.4%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean179.2740895
Minimum152.4
Maximum210.82
Zeros0
Zeros (%)0.0%
Memory size40.3 KiB
2021-03-02T16:08:55.456009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum152.4
5-th percentile165.1
Q1172.72
median180.34
Q3185.42
95-th percentile193.04
Maximum210.82
Range58.42
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation8.638977791
Coefficient of variation (CV)0.04818865802
Kurtosis0.05138414932
Mean179.2740895
Median Absolute Deviation (MAD)5.08
Skewness-0.09691256661
Sum921468.82
Variance74.63193727
MonotocityNot monotonic
2021-03-02T16:08:55.684376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
185.42630
12.2%
182.88623
12.1%
175.26507
9.9%
177.8506
9.8%
180.34489
9.5%
187.96405
7.9%
172.72388
7.5%
167.64353
6.9%
190.5326
6.3%
170.18304
5.9%
Other values (13)609
11.8%
ValueCountFrequency (%)
152.42
 
< 0.1%
154.9425
 
0.5%
157.487
 
0.1%
160.0263
1.2%
162.56114
2.2%
ValueCountFrequency (%)
210.8215
0.3%
208.283
 
0.1%
203.215
0.3%
200.666
 
0.1%
198.1229
0.6%

R_Weight_lbs
Real number (ℝ≥0)

Distinct58
Distinct (%)1.1%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean172.0758607
Minimum115
Maximum345
Zeros0
Zeros (%)0.0%
Memory size40.3 KiB
2021-03-02T16:08:55.978336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile125
Q1145
median170
Q3185
95-th percentile250
Maximum345
Range230
Interquartile range (IQR)40

Descriptive statistics

Standard deviation35.16407461
Coefficient of variation (CV)0.2043521645
Kurtosis0.828507353
Mean172.0758607
Median Absolute Deviation (MAD)15
Skewness0.9548348799
Sum884642
Variance1236.512143
MonotocityNot monotonic
2021-03-02T16:08:56.285151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1701034
20.1%
155891
17.3%
185744
14.5%
205507
9.9%
145493
9.6%
135487
9.5%
125268
 
5.2%
115129
 
2.5%
265105
 
2.0%
24077
 
1.5%
Other values (48)406
 
7.9%
ValueCountFrequency (%)
115129
 
2.5%
125268
 
5.2%
135487
9.5%
145493
9.6%
155891
17.3%
ValueCountFrequency (%)
3452
< 0.1%
3232
< 0.1%
3004
0.1%
2951
 
< 0.1%
2901
 
< 0.1%

B_age
Real number (ℝ≥0)

MISSING

Distinct31
Distinct (%)0.6%
Missing172
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean29.17196299
Minimum18
Maximum51
Zeros0
Zeros (%)0.0%
Memory size40.3 KiB
2021-03-02T16:08:56.566570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q126
median29
Q332
95-th percentile36
Maximum51
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.078538253
Coefficient of variation (CV)0.1398102093
Kurtosis0.3776576532
Mean29.17196299
Median Absolute Deviation (MAD)3
Skewness0.4408801328
Sum145043
Variance16.63447428
MonotocityNot monotonic
2021-03-02T16:08:56.816561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
28505
9.8%
29473
9.2%
30470
9.1%
27448
8.7%
31424
 
8.2%
26398
 
7.7%
25337
 
6.6%
32325
 
6.3%
33265
 
5.2%
24242
 
4.7%
Other values (21)1085
21.1%
ValueCountFrequency (%)
182
 
< 0.1%
192
 
< 0.1%
2019
 
0.4%
2147
0.9%
22109
2.1%
ValueCountFrequency (%)
511
 
< 0.1%
471
 
< 0.1%
462
< 0.1%
453
0.1%
443
0.1%

R_age
Real number (ℝ≥0)

MISSING

Distinct29
Distinct (%)0.6%
Missing64
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean29.44232283
Minimum19
Maximum47
Zeros0
Zeros (%)0.0%
Memory size40.3 KiB
2021-03-02T16:08:57.119807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23
Q126
median29
Q332
95-th percentile37
Maximum47
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.141927357
Coefficient of variation (CV)0.1406793676
Kurtosis-0.0535958144
Mean29.44232283
Median Absolute Deviation (MAD)3
Skewness0.2877907512
Sum149567
Variance17.15556223
MonotocityNot monotonic
2021-03-02T16:08:57.416702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
30469
 
9.1%
29467
 
9.1%
31442
 
8.6%
27439
 
8.5%
28432
 
8.4%
26397
 
7.7%
32392
 
7.6%
25306
 
5.9%
33291
 
5.7%
34268
 
5.2%
Other values (19)1177
22.9%
ValueCountFrequency (%)
196
 
0.1%
2025
 
0.5%
2143
 
0.8%
22100
1.9%
23174
3.4%
ValueCountFrequency (%)
471
 
< 0.1%
462
 
< 0.1%
451
 
< 0.1%
445
0.1%
433
0.1%

Interactions

2021-03-02T16:07:34.239457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:34.520866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:34.802134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:35.114649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:35.380152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:35.668334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:35.965372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:36.314735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:36.688820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:37.061543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:37.342815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:37.592688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:37.874093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:38.155223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:38.432266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:38.697912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:38.994807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:39.244817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:39.526082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:39.822982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:40.119833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:40.416764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:40.749078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:41.046114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:41.322170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:41.630989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:41.896768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:42.230385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:42.630370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:43.144498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:43.566610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:44.023842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:44.357501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:44.669988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:45.451270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:45.795089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:46.107609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:46.393396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:46.674657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:46.955926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:47.273056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:47.585426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:47.882325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:48.179213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:48.526898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:48.839419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:49.151797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:49.480087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:49.823834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:50.160238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:50.540340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:50.958472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:51.313214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:51.600925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:51.850941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:52.116581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:52.429083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:52.694744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:52.975874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:53.241657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:53.538552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:53.819821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:54.101085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:54.351078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:54.616747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:54.866764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:55.148034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:55.476033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:55.773073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:56.069825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:56.357014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:56.950759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:57.327935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:57.704881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:58.037964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:58.326461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:58.669361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:59.106892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:59.466434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:07:59.872689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:00.247596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:00.638244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:01.041076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:01.365156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:01.696726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:02.009248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:02.337403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:02.649921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:02.962440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:03.243704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:03.540573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:03.821862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:04.147330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:04.510546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:04.891647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:05.233209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:05.530101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:05.826971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:06.139513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:06.457976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:06.786112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:07.083002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:07.364269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:07.661182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:07.958083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:08.270579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:08.592707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:08.936619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:09.249141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:09.546032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:09.874156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:10.202312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:10.530467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:10.912587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:11.272703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:11.957807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:12.328465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:12.656492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:13.000389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:13.328553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:13.687813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:14.062978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:14.390984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:14.797257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:15.203538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:15.641206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:15.984980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:16.297499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:16.598953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:16.911472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:17.208345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:17.505122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:17.786532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:18.093916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:18.482896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:18.812122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:19.155448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:19.452466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:19.749376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:20.061901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:20.374422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:20.671171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:21.041240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:21.359307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:21.640579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:21.890597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:22.156361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:22.437627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:22.687664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:22.953287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:23.234555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:23.515848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:23.797093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:24.094007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:24.390884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:24.656550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:24.906535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:25.229951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:25.580993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:25.952135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:26.358147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:26.784869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:27.159900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:27.550553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:27.863213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:28.175736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:28.506972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:28.797955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:29.063455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:29.344746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:30.016743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:30.329313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:30.688567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:31.095591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:31.444819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:31.819983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:32.129879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:32.492973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:32.846184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:33.186162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:33.467402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T16:08:33.717443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-02T16:08:58.550535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-02T16:08:59.475554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-02T16:09:00.383895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-02T16:09:01.406939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-02T16:09:02.300552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-02T16:08:34.295463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-02T16:08:35.951966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-02T16:08:36.817437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-02T16:08:37.692638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0#R_fighterB_fighterRefereedatelocationWinnerweight_classno_of_roundsB_lossesB_total_rounds_foughtB_winsB_StanceB_Height_cmsB_Weight_lbsR_lossesR_total_rounds_foughtR_winsR_StanceR_Height_cmsR_Weight_lbsB_ageR_age
000Henry CejudoMarlon MoraesMarc Goddard2019-06-08Chicago, Illinois, USARedBantamweight51.09.04.0Orthodox167.64135.02.027.08.0Orthodox162.56135.031.032.0
111Valentina ShevchenkoJessica EyeRobert Madrigal2019-06-08Chicago, Illinois, USARedWomen's Flyweight56.029.04.0Orthodox167.64125.02.025.05.0Southpaw165.10125.032.031.0
222Tony FergusonDonald CerroneDan Miragliotta2019-06-08Chicago, Illinois, USARedLightweight38.068.023.0Orthodox185.42155.01.033.014.0Orthodox180.34155.036.035.0
333Jimmie RiveraPetr YanKevin MacDonald2019-06-08Chicago, Illinois, USABlueBantamweight30.09.04.0Switch170.18135.02.020.06.0Orthodox162.56135.026.029.0
444Tai TuivasaBlagoy IvanovDan Miragliotta2019-06-08Chicago, Illinois, USABlueHeavyweight31.08.01.0Southpaw180.34250.01.07.03.0Southpaw187.96264.032.026.0
555Tatiana SuarezNina AnsaroffRobert Madrigal2019-06-08Chicago, Illinois, USARedWomen's Strawweight32.018.04.0Orthodox165.10115.00.08.04.0NaN165.10115.033.028.0
666Aljamain SterlingPedro MunhozMarc Goddard2019-06-08Chicago, Illinois, USARedBantamweight34.023.08.0Orthodox167.64135.03.032.09.0Orthodox170.18135.032.029.0
777Karolina KowalkiewiczAlexa GrassoKevin MacDonald2019-06-08Chicago, Illinois, USABlueWomen's Strawweight32.010.02.0Orthodox165.10115.04.025.05.0Orthodox160.02115.025.033.0
888Ricardo LamasCalvin KattarDan Miragliotta2019-06-08Chicago, Illinois, USABlueFeatherweight31.010.03.0Orthodox180.34145.05.034.010.0Orthodox172.72145.031.037.0
999Yan XiaonanAngela HillRobert Madrigal2019-06-08Chicago, Illinois, USARedWomen's Strawweight36.026.04.0Orthodox160.02115.00.09.03.0Orthodox165.10115.034.029.0

Last rows

Unnamed: 0#R_fighterB_fighterRefereedatelocationWinnerweight_classno_of_roundsB_lossesB_total_rounds_foughtB_winsB_StanceB_Height_cmsB_Weight_lbsR_lossesR_total_rounds_foughtR_winsR_StanceR_Height_cmsR_Weight_lbsB_ageR_age
513451345134Patrick SmithRay WizardJohn McCarthy1994-03-11Denver, Colorado, USARedOpen Weight10.00.00.0NaNNaNNaN1.01.00.0Orthodox187.96225.0NaN30.0
513551355135Scott MorrisSean DaughertyJohn McCarthy1994-03-11Denver, Colorado, USARedOpen Weight10.00.00.0NaN182.88175.00.00.00.0Orthodox177.80210.018.0NaN
513651365136Royce GracieGerard GordeauJoao Alberto Barreto1993-11-12Denver, Colorado, USARedCatch Weight10.02.02.0Orthodox195.58216.00.02.02.0Southpaw185.42175.034.026.0
513751375137Jason DeLuciaTrent JenkinsJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.00.00.0NaN187.96185.00.00.00.0Southpaw180.34190.0NaN24.0
513851385138Royce GracieKen ShamrockJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.01.01.0Orthodox185.42205.00.01.01.0Southpaw185.42175.029.026.0
513951395139Gerard GordeauKevin RosierJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.01.01.0Orthodox193.04275.00.01.01.0Orthodox195.58216.0NaN34.0
514051405140Ken ShamrockPatrick SmithJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.00.00.0Orthodox187.96225.00.00.00.0Orthodox185.42205.030.029.0
514151415141Royce GracieArt JimmersonJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.00.00.0Orthodox185.42196.00.00.00.0Southpaw185.42175.030.026.0
514251425142Kevin RosierZane FrazierJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.00.00.0Orthodox195.58250.00.00.00.0Orthodox193.04275.0NaNNaN
514351435143Gerard GordeauTeila TuliJoao Alberto Barreto1993-11-12Denver, Colorado, USARedOpen Weight10.00.00.0Orthodox182.88430.00.00.00.0Orthodox195.58216.024.034.0